SOLVE magazine Issue 03 2021 - Page 27

“ There ’ s a lot of people out there who will give their time to advance science and I think that ’ s one of the lovely stories to come from Galaxy Zoo and a big reason it was evolved into Zooniverse ,” Professor Nichol says .
The challenge of big data has subsequently given rise to machine learning and artificial intelligence ( AI ) technology that hinges on the ability to make accurate classifications .
However , Galaxy Zoo never released humans from the classification role . The team found people are so much better at it and for an important reason – they can spot oddities and are drawn by curiosity to anomalies . It ’ s a trait that is not easy to train into an algorithm . And it is a trait that has given rise to important discoveries .
Professor Thomas explains that to keep the task simple , people were initially asked to distinguish elliptical from spiral galaxies . But it was soon noticed that the amateurs behaved like scientists .
“ They started emailing us and it became impossible to address all the queries . So we established a forum for addressing common queries ,” recalls Professor Nichol .
People also went off and did their own research and started reading up about astronomy .
“ There were quite a few interesting objects that were discovered that way ,” Professor Thomas says . “ We wrote proper science papers about things that armchair astronomers had discovered from their living rooms . It was really amazing .”
The upshot is that AI has not usurped the citizen scientists . On the contrary , the human-based SDSS galaxy classifications are now used as a dataset to train AI algorithms .
“ A human can find anomalies without any preconception of what such an anomaly would look like – it just looks odd ,” says Professor Nichol .
“ Computers still find it very difficult to find such ‘ unsupervised ’ anomalies and usually need to be trained extensively before knowing what is ‘ odd ’. Computers can find things that are part of their training set ; they ’ re less good at finding stuff they ’ ve never trained on . That ’ s an interesting and important difference .
“ There ’ s been an evolution where the heavy lifting in processing of the simple stuff can be done by machines and we use the humans for the more subtle , more difficult tasks .”
Eventually the Hubble Space Telescope data was added into the platform and the platform evolved into Zooniverse , which continues to expand . Along the way it proved that the human brain – including that of the untrained amateur – will always play a major part in pattern recognition in science and all its applications .
More information :
www . galaxyzoo . org
www . zooniverse . org with research that can better the world .
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